In proceedings of Central European Seminar on Computer Graphics for Students (CESCG'2013), pages 113-120, Apr. 2013

Abstract

The exploration of medical imaging datasets often requires a segmentation of the images according to different materials or structures. Model-based algorithms excel in finding closed boundary contours enclosing the structure to be segmented. However, porose structures like Spongiosa have a complex topology and do not exhibit a unique single closed boundary contour. In order to enable segmentation of such complex structures we suggest a new algorithmic framework based on a Reeb graph representing the topological information. Each node in the graph corresponds to a connected region of voxels in a specific image slice while edges indicate connected regions between adjacent slices. Starting with a coarse segmentation, the corresponding graph is refined at critical nodes and the resulting connected components of the graph provide the final segmentation. We present two strategies for identifying critical nodes, one solely based on dynamic thresholding and one based on a single user specified pre-segmentation. The approach is evaluated on a dataset of 193 muCT scans of rodent skulls which are segmented into skull, left and right mandible.

(Best paper award, third place.)

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Bibtex

@INPROCEEDINGS{wiens2013,
author = {Wiens, Vitalis},
pages = {113--120},
title = {Volumetric Segmentation of Complex Bone Structures from Medical Imaging Data Using Reeb Graphs},
booktitle = {Central European Seminar on Computer Graphics for Students (CESCG'2013)},
year = {2013},
month = apr,
abstract = {The exploration of medical imaging datasets often requires a segmentation of the images according to
different materials or structures. Model-based algorithms excel in finding closed boundary contours
enclosing the structure to be segmented. However, porose structures like Spongiosa have a complex
topology and do not exhibit a unique single closed boundary contour. In order to enable segmentation
of such complex structures we suggest a new algorithmic framework based on a Reeb graph representing
the topological information. Each node in the graph corresponds to a connected region of voxels in a
specific image slice while edges indicate connected regions between adjacent slices. Starting with a
coarse segmentation, the corresponding graph is refined at critical nodes and the resulting
connected components of the graph provide the final segmentation. We present two strategies for
identifying critical nodes, one solely based on dynamic thresholding and one based on a single user
specified pre-segmentation. The approach is evaluated on a dataset of 193 muCT scans of rodent
skulls which are segmented into skull, left and right mandible.}
}